As a key component of Industrial Internet, identification and resolution has been considered as a promising technology to realize the interconnections among physical and virtual entities and to promote interoperability of digital objects, which improves the industrial efficiency and transparency of the supply chain. In this article, we focus on the identification and resolution design in the Industrial Internet. A general identification and resolution architecture is proposed to guide how to develop and improve technologies and schemes in terms of the joint requirements of service, role, function, implement, and security in Industrial Internet. Based on the proposed architecture, three key technologies are studied. Specifically, an identifiable digital object (IDO) model is constructed to enable the different Industrial Internet platforms to manage and interact data uniformly. Then, a hybrid structure-based identification and resolution system is designed, based on which identification registration and resolution procedures are proposed. Furthermore, a trustable system based on blockchain is deployed to guarantee the data credibility. Finally, extensive practical experiments validate the effectiveness of the proposed technologies. The proposed architecture and technologies have been used in the practical construction and deployment of national top-level nodes and secondary-level nodes to sustain the identification and resolution services for the Industrial Internet in China.

In order to make full use of the network data and guarantee user privacy simultaneously, federated learning (FL) is proposed to enable distributed intelligence for local nodes without sharing data with each other. However, in practice, due to resource limitations, traditional FL suffers from node scheduling and parameter transmission failure, which not only affects the final performance but also further reduces the fairness of the participating nodes. This article addresses the challenge and proposes an FL method to enhance the performance of FL on the basis of guaranteeing the fairness of the local nodes in a resource-constrained Internet of Things (IoT) network. Specifically, an analytical model is first constructed to characterize the performance of FL with joint considerations of node fairness, unreliable parameter transmissions as well as resource limitations. Thereafter, a statistically reweighted aggregation (SRA) scheme is proposed for parameter aggregation and the corresponding model is proved to be unbiased to that based on ideal parameter transmissions. With the knowledge of time dependency of the global model, we further extend SRA and propose a reliable SRA (RSRA) scheme. Additionally, we prove RSRA is able to achieve higher stability performance than SRA in model training. Furthermore, the convergence bound of the proposed RSRA is derived analytically, based on which an adaptive local training scheme is proposed under a given resource budget. Finally, extensive experiments are carried out with a public data set to validate the effectiveness of the proposed scheme with comparisons of other baseline schemes.

Due to the rapid growth of web conferences, remote screen sharing, and online games, screen content has become an important type of internet media information and over 90% of online media interactions are screen based. Meanwhile, as the main component in the screen content, textual information averagely takes up over 40% of the whole image on various commonly used screen content datasets. However, it is difficult to compress the textual information by using the traditional coding schemes as HEVC, which assumes strong spatial and temporal correlations within the image/video. State-of-the-art screen content coding (SCC) standard as HEVC-SCC still adopts a block-based coding framework and does not consider the text semantics for compression, thus inevitably blurring texts at a lower bitrate. In this paper, we propose a general text semantic-aware screen content coding scheme (TSA-SCC) for ultra low bitrate setting. This method detects the abrupt picture in a screen content video (or image), recognizes textual information (including word, position, font type, font size and font color) in the abrupt picture based on neural networks, and encodes texts with text coding tools. The other pictures as well as the background image after removing texts from the abrupt picture via inpainting, are encoded with HEVC-SCC. Compared with HEVC-SCC, the proposed method TSA-SCC reduces bitrate by up to $3\times $ at a similar compression quality. Moreover, TSA-SCC achieves much better visual quality with less bitrate consumption when encoding the screen content video/image at ultra low bitrates.

The high performance requirement of task processing and low energy consumption requirement of network operation result in a contradiction for the Internet of Vehicles (IoV). This paper addresses the contradiction and proposes an efficient edge-terminal collaboration scheme to sustain the diverse task requirements for a green IoV network. Specifically, two types of tasks are first modeled in terms of data size and delay requirements and then an energy minimization problem was formulated under the diverse task requirements. Thereafter, a multi-agent reinforcement learning framework is constructed, based on which the time-varying environment of the IoV can be modeled as a partially observable Markov decision process. Furthermore, a multi-agent soft actor-critic (MASAC) scheme is proposed to train the edge-terminal collaboration policy in terms of task offloading, spectrum sharing as well as vehicle power control. Finally, the effectiveness of the proposed scheme is validated by comparing with other baseline schemes through extensive simulation experiments.

This article focuses on the service freshness guarantee for cooperative sensing in the Internet of Everything. Specifically, the peak Age of Information (AoI) is first introduced to evaluate the information and service freshness. An analytical model is then constructed to decouple the components of peak AoI into the interarrival time and transmission time. With the knowledge of identical increments of update arrival and transmission process, a close bound of peak AoI violation probability is derived based on martingale theory. Furthermore, the impact of source node parameter configuration on the peak AoI violation probability are analyzed and a task allocation scheme is proposed to guide cooperative sensing. Numerical analysis validates the tightness of the peak AoI violation probability bound and the effectiveness of the proposed scheme in service freshness guarantee.

Social Internet of thing (SIoT) is an emerging area that introduces sociology theories into IoT networks, in order to support the spread behaviour of multimedia contents. With the explosive growth of data in the internet, traditional solutions are no longer suitable for large-scale SIoT networks. To address this challenge, this work target to optimize the information propagation in SIoT networks. First, we establish a novel model to divide SIoT network into communities on the basis of network social attributes. Second, we propose an information dissemination control mechanism based on variation trends of content popularity, in order to maximize the spread performance of information. Third, we check the relations between performance indicators and network attributes via a computer simulation. Simulation results show the feasibility of the proposed mechanism. Simulations results also reveal that the proposed mechanism make better balance compared with existing solutions in terms of efficiency and complexity.